MRI-based radiomics to predict response in locally advanced rectal cancer: comparison of manual and automatic segmentation on external validation in a multicentre study.
Arianna DefeudisSimone MazzettiJovana PanicMonica MicilottaLorenzo VassalloGiuliana GiannettoMarco GattiRiccardo FalettiStefano CirilloDaniele ReggeValentina GianniniPublished in: European radiology experimental (2022)
Our study showed that radiomics models can pave the way to help clinicians in the prediction of tumour response to chemoradiotherapy of LARC and to personalise per-patient treatment. The results from the external validation dataset are promising for further research into radiomics approaches using both manual and automatic segmentations.
Keyphrases
- rectal cancer
- locally advanced
- contrast enhanced
- deep learning
- lymph node metastasis
- neoadjuvant chemotherapy
- squamous cell carcinoma
- phase ii study
- magnetic resonance imaging
- radiation therapy
- machine learning
- computed tomography
- magnetic resonance
- convolutional neural network
- palliative care
- diffusion weighted imaging
- combination therapy
- open label
- study protocol